Analysis method for standardization reviews on Beijing enterprises

doi:10.16511/j.cnki.qhdxxb.2018.25.034

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Abstract There are over one million items from ten thousand enterprises in the Beijing Enterprise Standardization Review. Big data analytical methods can be used to analyze the relationships between the deduction counts of the review items because of the large data volume. The most popular method is association rules, but these are qualitative, not quantitative. Neural network, another widely used data mining method, are able to solve complex non-linear problems but requires much effort to choose the suitable inputs and targets. This article combines these two methods with the association rules used to select the inputs and targets from the review items and the neural network used to relate the inputs and the targets. A test gave a strong correlation between 3 selected review items and 8 other review items with a correlation coefficient of the fitting curve of over 0.84 between the predicted targets and the real value. Thus, this combined method can improve data mining of the enterprises review items with the result used to predict the deduction counts of the selected items.

Keywords production safety association rules frequency pattern growth back propagation neural networ

Issue Date: 15 August 2018

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	ZHU Yu
	CHEN Tao
	JI Xuewei
	ZHANG Hui
	WU Aizhi

Cite this article:

ZHU Yu,CHEN Tao,JI Xuewei, et al. Analysis method for standardization reviews on Beijing enterprises[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(8): 768-772.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.25.034 OR http://jst.tsinghuajournals.com/EN/Y2018/V58/I8/768

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